Mitochondrial genome annotation with MFannot: a critical analysis of gene identification and gene model prediction

Abstract

Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.